Question 1

How to detect duplicate images with goimagehash?

Accepted Answer

Compute hashes using functions like AverageHash or DifferenceHash on your images, then compare them using the Distance method to get a Hamming distance. A low distance (e.g., below a threshold like 10) typically indicates similarity, as shown in the main example.

Question 2

What's the best hashing algorithm in goimagehash for resized images?

Accepted Answer

Perception hashing is often more robust to resizing and compression due to its use of discrete cosine transform (DCT), while difference hashing can handle gradients well. Test with your specific images to choose, as accuracy varies by use case.

Question 3

How to save and reload image hashes in goimagehash?

Accepted Answer

Use the Dump method to serialize a hash to a writer (e.g., bytes.Buffer) and LoadImageHash or LoadExtImageHash to deserialize from a reader. This is useful for storing hashes in databases, as demonstrated in the code with bufio.

Question 4

Is goimagehash compatible with older versions?

Accepted Answer

No, v1.0.0 has breaking changes and is not backward compatible with pre-v1.0.0 versions. You'll need to update your code if migrating, as highlighted in the release notes under v1.0.0.

Question 5

goimagehash vs imagehash Python library: which is better?

Accepted Answer

goimagehash is a Go port inspired by imagehash, offering similar algorithms with Go's performance benefits for concurrent applications. However, imagehash might have more mature features or community support, so evaluate based on language and project needs.

Question 6

How to customize hash bit size in goimagehash?

Accepted Answer

Use the extended hash APIs like ExtAverageHash with width and height parameters to set the hash dimensions. For example, ExtAverageHash(img, 16, 16) creates a 256-bit hash, affecting sensitivity and comparison granularity.

goimagehash

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